An improved building detection approach using L-band POLSAR two-dimensional time-frequency decomposition over oriented built-up areas

The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. This paper develops an improved approach for building detection by utilizing Two-Dimensional Time-Frequency (2-D TF) decomposition. This method performs outstandingly in distinguishing between man-made and natural targets based on the isotropic behaviors, frequency-sensitive responses, and scattering mechanisms of objects. The proposed method can preserve the spatial resolution and exploit the advantages of TF decomposition; specifically, the exact outlines of buildings can be effectively located, and more types of features (e.g., flat roofs, roads, and walls that are oblique to the radar illumination) can be distinguished from forests in complex built-up areas by 2-D TF decomposition. The coarser-resolution subaperture images that are produced in the azimuth direction, which correspond to different looking angles, are beneficial for detecting man-made structures with main scattering centers oriented at oblique angles with respect to the radar illumination. In the range direction, the obtained subaperture images, which correspond to various observation frequencies, can be helpful in distinguishing flat roofs and roads from forests. This method was successfully implemented to analyze both NASA/JPL L-band AIRSAR and L-band EMISAR data sets. The building detection results of the proposed method exhibit a significant improvement over those of other methods and reach an overall accuracy over 80%, with approximately 20% higher than the accuracies of K-means clustering and the entropy/alpha-Wishart classifier and approximately 10% higher than the accuracy of the support vector machine method. Moreover, building details can be precisely detected, obliquely oriented buildings can be identified, and the distinction between buildings and forests is significantly improved, as both visually and statistically indicated. This method is highly adaptable and has substantial application value.     

基于激光点云的巨型倾倒大变形体监测研究

以某特高岸水库巨型变形体为研究对象,应用地面三维激光扫描仪采集变形体表面激光点云数据,建立变形体DEM模型,几何量测提取点、线、面(体)成果,对比及叠加分析,得到特定点、线、面及整个变形体位移量信息。研究结果表明,高密度激光点云数据可弥补传统监测成果单一及分析局限或以点概全的不足,以其丰富多样的监测成果全面反映变形体的变形信息,尤其对速滑变形在时效性和精度匹配上更有效,可达到应急预报要求。     

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.     

灰关联GM(1,N)模型在建筑物沉降分析中的应用

针对建筑物沉降变形中各监测点相互关联、相互影响的情形,可将灰色系统理论中的GM(1,N)模型引入到建筑物沉降分析中。本文利用灰度关联方法确定相关因子的关联度,建立GM(1,N)模型,并与回归分析、GM(1,1)模型比较。通过工程实例,得出灰关联GM(1,N)模型预测精度明显高于另外两种模型,验证了该模型在进行建筑物沉降分析预测中应用的可行性。     

Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data.     

Impact of different sampling rates on precise point positioning performance using online processing service

In this study, the effect of different sampling rates (i.e. observation recording interval) on the Precise Point Positioning (PPP) solutions in terms of accurac...     

不同草地类型净初级生产力(NPP)模拟及其敏感性分析

气候变化问题作为人类社会可持续发展面临的重大挑战,受到国际社会越来越强烈的关注.全球气候变化深刻影响着草地生态系统,定量评估区域和不同类型草地生态系统的生产力,研究其对气候变化的敏感性可以为草地生态系统适应未来气候变化提供基础数据和理论依据.草原综合顺序分类系统(CSCS)将天然草原分为42类(其中中国包含41类),并...     

东昆仑造山带海德乌拉铀矿床成矿流体特征研究

东昆仑造山带海德乌拉铀矿床是近些年西北地区最新探明的与火山岩有关的独立铀矿床,该矿床的发现为东昆仑造山带探寻热液型铀矿床提供了指示意义。本文选择与海德乌拉铀矿成矿期相关的透明矿物（粉红色方解石、紫黑色萤石及石英）作为研究对象,系统地开展C- H- O同位素和流体包裹体研究,查明该矿床成矿流体的来源与性质,并探讨矿床成因。研究结果表明,海德乌拉铀矿床成矿期石英中主要为H2O气液两相包裹体,少见CO2- H2O两相包裹体;在粉红色方解石脉、紫黑色萤石脉中流体包裹体均含H2O气液两相包裹体,在粉红色方解石脉中偶见纯液相包裹体,均未见到纯气相及含固相包裹体。成矿期粉红色方解石、紫黑色萤石及石英中包裹体均一温度范围分别为133~187℃（均值163℃）、127~204℃（均值169℃）、183~287℃（均值219℃）,盐度范围分别为1. 40%~7. 02%NaCleq（均值3. 65%NaCleq）、0. 53%~3. 06%NaCleq（均值1. 26%NaCleq）、7. 17%~17. 26%NaCleq（均值为11. 46%NaCleq）。流体包裹体气相成分以H2O为主,另含少量CO2等。C- H- O同位素实验数据表明,流体中δ13CFluid- V- PDB、δDFluid- V- SMOW、δ18OFluid- V- SMOW值的变化范围分别为1. 59‰~1. 00‰、71‰~63‰、0. 03‰~3. 72‰,表明成矿流体并非单一来源,可能为大气降水与岩浆水混合来源。此外,沥青铀矿的沉淀主要是由于流体与围岩的相互反应所引起的物理化学条件变化加上流体沸腾/CO2去气,最终导致了沥青铀矿等成矿物质发生大规模的卸载与沉淀。     

人工冻土观测的冻点对应的土壤温度研究

基于2018年12月至2020年3月喀左、沈阳、辽阳、满洲里4个国家级地面气象站人工冻土器与测温式冻土自动观测仪观测的资料,对人工冻土观测获得的冻点与测温式冻土自动观测仪获得的相应深度的温度进行对比分析.结果表明:人工冻土器获取的冻点对应的土壤温度与0℃总体一致,又不完全重合;0—35 cm深度范围,冻点对应的温度变化...     

渤海裸甲藻和链状亚历山大藻的麻痹性贝毒毒素分析

目的:分析渤海裸甲藻(Gymnodinium sp.)和链状亚历山大藻(Alexandrium catenella)的麻痹性贝毒毒素,为渤海天津海域的赤潮研究积累基础数据。方法:通过实验室培养裸甲藻和链状亚历山大藻,选取对数生长期、平台生长期的裸甲藻以及平台生长期的链状亚历山大藻,利用高效液相色谱法(HPLC)对这两种微藻进行麻痹性贝毒(PSP)毒素分析。结果:裸甲藻细胞内不含有麻痹性贝毒(PSP);链状亚历山大藻细胞内含有C毒素和GTX1-4毒素,该微藻每个细胞毒素含量约为10.81 fmol/cell。结论:裸甲藻细胞内虽不含有麻痹性贝毒(PSP),但不能排除其含有其它毒素的可能。链状亚历山大藻细胞内含有麻痹性贝毒(PSP),属于有毒微藻,需要对其进行密切监测。